Text Summarization With Graph Attention Networks
arXiv:2604.03583v1 Announce Type: new Abstract: This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance of our baseline summarization models. Specifically, we experimented with a Graph Attention Network architecture to incorporate graph information. However, this architecture did not enhance the performance. Subsequently, we used a simple Multi-layer Perceptron architecture, which improved the results in our proposed model on our primary dataset, CNN/DM. Additionally, we annotated XSum dataset with RST graph information, establishing a benchmark for future graph-based summarization models. This secondary dataset posed multiple challenges, revealing both the merits and limitations of our models.
arXiv:2604.03583v1 Announce Type: new Abstract: This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance of our baseline summarization models. Specifically, we experimented with a Graph Attention Network architecture to incorporate graph information. However, this architecture did not enhance the performance. Subsequently, we used a simple Multi-layer Perceptron architecture, which improved the results in our proposed model on our primary dataset, CNN/DM. Additionally, we annotated XSum dataset with RST graph information, establishing a benchmark for future graph-based summarization models. This secondary dataset posed multiple challenges, revealing both the merits and limitations of our models.
Executive Summary
This study explores the application of graph attention networks (GATs) and multi-layer perceptron (MLP) architectures to enhance text summarization performance. The authors leverage Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs to incorporate graph information. Although the GAT architecture failed to improve performance, the simple MLP architecture showed promising results on the primary CNN/DM dataset. The study also establishes a benchmark for future graph-based summarization models by annotating the XSum dataset with RST graph information. However, the secondary dataset posed significant challenges, revealing both the merits and limitations of the proposed models. This research contributes to the ongoing development of text summarization models and highlights the importance of exploring different architectures and datasets in this field.
Key Points
- ▸ The study explores the application of graph attention networks (GATs) to text summarization
- ▸ The GAT architecture failed to improve performance, while the MLP architecture showed promising results
- ▸ The study establishes a benchmark for future graph-based summarization models using the XSum dataset
Merits
Strength of Graph-Based Approach
The study effectively leverages graph information to enhance text summarization performance, demonstrating the potential of graph-based approaches in this field.
Improvement in CNN/DM Dataset
The simple MLP architecture showed promising results on the primary CNN/DM dataset, indicating the potential of this architecture for text summarization tasks.
Demerits
Limitation of GAT Architecture
The GAT architecture failed to improve performance, suggesting potential limitations of this architecture for text summarization tasks.
Challenges Posed by XSum Dataset
The secondary XSum dataset posed significant challenges, revealing both the merits and limitations of the proposed models and highlighting the need for further research in this area.
Expert Commentary
The study's findings are significant in the field of text summarization, as they demonstrate the potential of graph-based approaches and highlight the importance of exploring different architectures and datasets. However, the limitations of the GAT architecture and the challenges posed by the XSum dataset highlight the need for further research in this area. Future studies should focus on developing more effective graph-based approaches and exploring their potential for various text summarization tasks.
Recommendations
- ✓ Recommendation 1: Future studies should explore the development of more effective graph-based approaches for text summarization tasks.
- ✓ Recommendation 2: Researchers should continue to investigate the potential of different architectures and datasets in text summarization, with a focus on leveraging graph information to enhance performance.
Sources
Original: arXiv - cs.CL